Probabilistic Analysis of Blocking Attack in RFID Systems
Bibliographic record
Abstract
Radio-frequency identification (RFID) is a ubiquitous wireless technology which allows objects to be identified automatically. An RFID tag is a small electronic device with an antenna and has a unique serial number. Using RFID tags can simplify many applications and provide many benefits. Meanwhile, the privacy of the customers should be taken into account. A potential threat for the privacy of a user is that of anonymous readers obtaining information about the tags in the system. The use of a blocker tag has been proposed as a solution to avoid unwanted tag interrogations. A blocker tag can simulate all or a portion of tag IDs in the system. This prevents the malicious readers from identifying the tags and obtaining information from the system. Although this solution is simple to implement and has a low cost, it may add another threat to the RFID system if used as a malicious tool to attack the system. A malicious blocker tag can deteriorate the performance of an RFID system by simulating fake tag IDs. In this paper, we study the use of blocker tags for malicious attacks that can prevent nearby legitimate readers from correctly receiving the reply messages from the tags. The blocker attack is a medium access control (MAC)-layer denial of service (DoS) threat and we propose a lower-layer solution for this attack. We mathematically model the blocker attack for RFID systems which operate based on the binary tree walking or ALOHA singulation techniques. Using the developed analytical framework, we propose a probabilistic blocker tag detection (P-BTD) algorithm to detect the presence of an attacker in the RFID system. The P-BTD algorithm can detect the existence of a blocker tag using the information extracted from the interrogations performed by the reader. Simulation results show that our proposed algorithm has a better performance than the threshold-based detection algorithm in terms of the number of required interrogations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".